Madallah Alruwaili

1.7k total citations
57 papers, 973 citations indexed

About

Madallah Alruwaili is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Madallah Alruwaili has authored 57 papers receiving a total of 973 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 16 papers in Artificial Intelligence and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Madallah Alruwaili's work include COVID-19 diagnosis using AI (10 papers), AI in cancer detection (7 papers) and IoT and Edge/Fog Computing (5 papers). Madallah Alruwaili is often cited by papers focused on COVID-19 diagnosis using AI (10 papers), AI in cancer detection (7 papers) and IoT and Edge/Fog Computing (5 papers). Madallah Alruwaili collaborates with scholars based in Saudi Arabia, Pakistan and Egypt. Madallah Alruwaili's co-authors include Saad Alanazi, Muhammad Hameed Siddiqi, M. M. Kamruzzaman, Nasser Alshammari, Yousef Alhwaiti, Walaa Gouda, Fahad Ahmad, Abdulaziz Shehab, Md Nazirul Islam Sarker and Sameh Abd El-Ghany and has published in prestigious journals such as PLoS ONE, Scientific Reports and Computers in Human Behavior.

In The Last Decade

Madallah Alruwaili

54 papers receiving 911 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Madallah Alruwaili Saudi Arabia 19 326 204 179 151 134 57 973
Saad Alanazi Saudi Arabia 20 376 1.2× 165 0.8× 145 0.8× 174 1.2× 135 1.0× 84 1.1k
Jaehyuk Cha South Korea 18 300 0.9× 200 1.0× 219 1.2× 386 2.6× 125 0.9× 86 1.2k
Ramgopal Kashyap India 19 230 0.7× 108 0.5× 128 0.7× 165 1.1× 104 0.8× 52 987
Atef Zaguia Saudi Arabia 19 326 1.0× 184 0.9× 245 1.4× 179 1.2× 56 0.4× 51 1.2k
Sushruta Mishra India 21 288 0.9× 95 0.5× 155 0.9× 140 0.9× 79 0.6× 87 1.1k
Prabhu Jayagopal India 15 222 0.7× 135 0.7× 163 0.9× 86 0.6× 85 0.6× 83 826
Ibrahim Abunadi Saudi Arabia 18 363 1.1× 213 1.0× 156 0.9× 115 0.8× 42 0.3× 46 911
Sapna Juneja India 21 336 1.0× 88 0.4× 162 0.9× 242 1.6× 107 0.8× 85 1.2k
Avinash Sharma India 18 195 0.6× 80 0.4× 151 0.8× 206 1.4× 135 1.0× 113 964
Saeed Ali Bahaj Saudi Arabia 18 486 1.5× 185 0.9× 169 0.9× 510 3.4× 138 1.0× 54 1.2k

Countries citing papers authored by Madallah Alruwaili

Since Specialization
Citations

This map shows the geographic impact of Madallah Alruwaili's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Madallah Alruwaili with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Madallah Alruwaili more than expected).

Fields of papers citing papers by Madallah Alruwaili

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Madallah Alruwaili. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Madallah Alruwaili. The network helps show where Madallah Alruwaili may publish in the future.

Co-authorship network of co-authors of Madallah Alruwaili

This figure shows the co-authorship network connecting the top 25 collaborators of Madallah Alruwaili. A scholar is included among the top collaborators of Madallah Alruwaili based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Madallah Alruwaili. Madallah Alruwaili is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Alruwaili, Madallah, et al.. (2025). An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification. Diagnostics. 15(5). 551–551. 7 indexed citations
3.
Alruwaili, Madallah, et al.. (2025). Advancing EEG-based biometric identification through multi-modal data fusion and deep learning techniques. Complex & Intelligent Systems. 11(9).
4.
Alruwaili, Madallah, et al.. (2025). LSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations. Scientific Reports. 15(1). 6011–6011. 4 indexed citations
5.
Tawfeek, Medhat A., Ibrahim Alrashdi, Madallah Alruwaili, Warda M. Shaban, & Fatma M. Talaat. (2025). Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans. Neural Computing and Applications. 37(17). 11459–11477. 3 indexed citations
6.
7.
Siddiqi, Muhammad Hameed, Muhammad Zaffwan Idris, & Madallah Alruwaili. (2023). FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis. Healthcare. 11(12). 1713–1713.
8.
Elaraby, Ahmed, et al.. (2023). An Optimized Deep Learning Approach for Robust Image Quality Classification. Traitement du signal. 40(4). 1573–1579. 1 indexed citations
9.
Siddiqi, Muhammad Hameed, et al.. (2023). Development of a Smart Signalization for Emergency Vehicles. Sensors. 23(10). 4703–4703. 6 indexed citations
10.
Alanazi, Saad, Fahad Ahmad, Nasser Alshammari, et al.. (2022). Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques. International Journal of Environmental Research and Public Health. 19(15). 9695–9695. 22 indexed citations
11.
Alanazi, Saad, M. M. Kamruzzaman, Md Nazirul Islam Sarker, et al.. (2021). Boosting Breast Cancer Detection Using Convolutional Neural Network. Journal of Healthcare Engineering. 2021. 1–11. 108 indexed citations
12.
Elaraby, Ahmed, et al.. (2021). Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer. Computers, materials & continua/Computers, materials & continua (Print). 71(2). 4019–4031. 51 indexed citations
13.
Alshammari, Nasser, Md Nazirul Islam Sarker, M. M. Kamruzzaman, et al.. (2021). Technology‐driven 5G enabled e‐healthcare system during COVID‐19 pandemic. IET Communications. 16(5). 449–463. 21 indexed citations
14.
Ahmad, Fahad, Madallah Alruwaili, Z.A. Alrowaili, et al.. (2021). Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms. Computers, materials & continua/Computers, materials & continua (Print). 67(1). 641–657. 43 indexed citations
15.
Ahmad, Fahad, Madallah Alruwaili, Saad Alanazi, et al.. (2021). Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment. Computers, materials & continua/Computers, materials & continua (Print). 66(3). 2723–2749. 25 indexed citations
16.
Alruwaili, Madallah, Abdulaziz Shehab, & Sameh Abd El-Ghany. (2021). COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images. Journal of Healthcare Engineering. 2021. 1–16. 23 indexed citations
17.
Alanzi, Ayed R. A., et al.. (2020). Using Derived kernel as a new Method for Recognition a Similarity Learning.. International Journal of Engineering and Advanced Technology. 9(3). 1974–1980. 1 indexed citations
18.
Humayun, Mamoona, et al.. (2020). Privacy Protection and Energy Optimization for 5G-Aided Industrial Internet of Things. IEEE Access. 8. 183665–183677. 66 indexed citations
19.
Siddiqi, Muhammad Hameed, Madallah Alruwaili, & Amjad Ali. (2019). A Novel Feature Selection Method for Video-Based Human Activity Recognition Systems. IEEE Access. 7. 119593–119602. 11 indexed citations
20.
Alruwaili, Madallah, et al.. (2019). Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields. Computational Intelligence and Neuroscience. 2019. 1–14. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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